Separator: Sifting Hierarchical Heavy Hitters Accurately from Data Streams

Authors:
Yuan Lin;Hongyan Liu
Affiliations:
Dept. of Management Science and Engineering, Tsinghua University, Beijing 100084, China;Dept. of Management Science and Engineering, Tsinghua University, Beijing 100084, China
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

Citing 12
Cited 0

Synopsis data structures for massive data sets

External memory algorithms
New directions in traffic measurement and accounting

IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Data streams: algorithms and applications

SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Finding Frequent Items in Data Streams

ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Frequency Estimation of Internet Packet Streams with Limited Space

ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
What's hot and what's not: tracking most frequent items dynamically

Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications

Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Space complexity of hierarchical heavy hitters in multi-dimensional data streams

Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining data streams: a review

ACM SIGMOD Record
Approximate frequency counts over data streams

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Finding hierarchical heavy hitters in data streams

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Efficient computation of frequent and top-k elements in data streams

ICDT'05 Proceedings of the 10th international conference on Database Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present a new algorithm, Separator, for accurate and efficient Hierarchical Heavy Hitter (HHH) detection, an emerging research area of data stream mining. Existing algorithms exploit either bottom-up or top-down processing strategy to solve this problem, whereas we propose a novel combination of these two strategies. Based on this strategy and a devised compact data structure, we implement our algorithm. It is theoretically proved to have tight error bound and small space usage. Comprehensive experiments conducted also verify its accuracy and efficiency.